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from mmengine.config import read_base |
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from opencompass.partitioners import SizePartitioner |
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from opencompass.models import HuggingFaceCausalLM |
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from opencompass.runners import LocalRunner |
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from opencompass.partitioners import SizePartitioner |
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from opencompass.tasks import OpenICLInferTask |
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with read_base(): |
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from .datasets.humaneval.humaneval_passk_gen_8e312c import humaneval_datasets |
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from .datasets.mbpp.mbpp_passk_gen_1e1056 import mbpp_datasets |
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from .datasets.mbpp.sanitized_mbpp_passk_gen_1e1056 import sanitized_mbpp_datasets |
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datasets = [] |
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datasets += humaneval_datasets |
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datasets += mbpp_datasets |
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datasets += sanitized_mbpp_datasets |
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models = [ |
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dict( |
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type=HuggingFaceCausalLM, |
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abbr='CodeLlama-7b-Python', |
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path="codellama/CodeLlama-7b-Python-hf", |
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tokenizer_path='codellama/CodeLlama-7b-Python-hf', |
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tokenizer_kwargs=dict( |
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padding_side='left', |
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truncation_side='left', |
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trust_remote_code=True, |
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), |
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max_out_len=1024, |
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max_seq_len=2048, |
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batch_size=8, |
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model_kwargs=dict(trust_remote_code=True, device_map='auto'), |
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generation_kwargs=dict( |
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num_return_sequences=10, |
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do_sample=True, |
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top_p=0.95, |
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temperature=0.8, |
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), |
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run_cfg=dict(num_gpus=1, num_procs=1), |
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), |
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] |
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infer = dict( |
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partitioner=dict(type=SizePartitioner, max_task_size=300), |
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runner=dict( |
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type=LocalRunner, max_num_workers=16, |
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task=dict(type=OpenICLInferTask)), |
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) |
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